Proactive event driven computing

ABSTRACT

A method for predicting a future situation based on an analysis of at least one predictive pattern. The method comprises monitoring a plurality of events carried out by an event processing component, detecting a predictive pattern predictive of a future situation, selecting one of a plurality of proactive actions and an execution time according to its effect on at least one of a probability of occurrence and a cost of occurrence of the future situation, and outputting the selected proactive action.

BACKGROUND

The present invention, in some embodiments thereof, relates to eventprocessing and, more specifically, but not exclusively, to proactiveevent processing.

During the last years, event processing has become a commoninfrastructure for real-time applications which require processing ofdata streams, such as electronic trading systems, and networkmonitoring. Event processing systems are capable of detecting complexsituations (a context-sensitive composition of messages and events),rather than single events. The functionality is applicable whenprocessing a composition of event sources from a business, application,or infrastructure perspective within different contexts. Samplescenarios relating to SLA alerts and compliance checking are applicableto retail and banking.

An event (e) is an occurrence within a particular system or domain; itis something that has happened, or is contemplated as having happened inthat domain. An event type (E) is a specification for a set of eventsthat share the same semantic intent and structure. An event type canrepresent an event arriving from a producer, or an event produced by anevent processing agent (EPA) which is a processing element that applieslogic on a set of input events, to generate one or more outputs(derived) events. Events may be raw events and derived events. Derivedevents are higher-level events in the semantic hierarchy. As an example,bank deposit is an observable event; money laundering is higher levelevent that is inferred based on specific patterns over these bankdeposit events.

An event processing network (EPN) is a conceptual model, describing theevent processing flow execution. It consists of a collection of EPAs,producers, and consumers linked by channels. An event channel is aprocessing element that receives events from one or more source elements(producer or EPA), makes routing decisions, and sends the input eventsunchanged to one or more target elements (EPA or consumer) in accordancewith the routing decisions. The EPN follows an event drivenarchitecture, namely EPAs are communicating in asynchronous fashion byreceiving and sending events.

SUMMARY

According to an aspect of some embodiments of the present inventionthere is provided a method for predicting a future situation based on ananalysis of at least one predictive pattern. The method comprisesmonitoring a plurality of events carried out by an event processingcomponent, detecting a predictive pattern predictive of a futuresituation, selecting one of a plurality of proactive actions and anexecution time for the selected proactive action according to its effectwhen executed in the execution time on at least one of a probability ofoccurrence and a cost of occurrence of the future situation, andoutputting the selected proactive action.

According to an aspect of some embodiments of the present inventionthere is provided a system of predicting a future situation based on ananalysis of at least one predictive pattern. The system comprises aprocessor, a monitoring which monitors an event processing componentcarrying out a plurality of events, a detection module which detects apredictive pattern predictive of a future situation, and a predictionmodule which identifies a plurality of proactive actions each having aneffect on at least one of a probability of occurrence and a cost ofoccurrence of the future situation and selects, using the processor, oneof the plurality of proactive actions according to the effect.

According to an aspect of some embodiments of the present inventionthere is provided a computer program product for predicting a futuresituation based on an analysis of at least one predictive pattern. Thecomputer program product comprises a computer readable storage medium,first program instructions to monitor a plurality of events carried outby an event processing component, second program instructions to detecta predictive pattern predictive of a future situation, and third programinstructions to select one of a plurality of proactive actions and anexecution time according to its effect on at least one of a probabilityof occurrence and a cost of occurrence of the future situation. Thefirst, second, and third program instructions are stored on the computerreadable storage medium.

According to an aspect of some embodiments of the present inventionthere is provided a method for predicting a future situation based on ananalysis of at least one predictive pattern. The method comprisesmonitoring a plurality of events carried out by an event processingcomponent, detecting a predictive pattern predictive of a futuresituation, identifying a predefined rule that defines which of aplurality of proactive actions to take in response to the detection froma plurality of predefined rules, and performing the proactive action inresponse to the detection. The predefined rule is automaticallycalculated according to an effect of the proactive action on at leastone of a probability of occurrence and a cost of occurrence of thefuture situation in relation to the effect of other the plurality ofproactive actions.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a flowchart of a method a flowchart of a method 100 ofpredicting and reacting to a future situation, such as desired and/orundesired events in an event processing component, according to someembodiments of the present invention;

FIG. 2A is a relational view of software components of a system forpredicting a future situation based on the detection of a current eventpattern in a plurality of events carried out by an event processingcomponent, according to one embodiment of the invention;

FIG. 2B is a schematic illustration of a model that is operatedaccording to the pattern “detect-forecast-decide-act” to proactivelyreact to future situation of a physical system based on analysis of itsevents in real time, according to some embodiments of the presentinvention;

FIG. 3 is a graph depicting an exemplary probabilistic structure whereinthe probability a future situation is measured according to adistribution, according to some embodiments of the present invention;and

FIGS. 4A and 4B depicts states and paths there between, according tosome embodiments of the present invention.

DETAILED DESCRIPTION

The present invention, in some embodiments thereof, relates to eventprocessing and, more specifically, but not exclusively, to proactiveevent processing.

According to some embodiments of the present invention, there areprovided methods and systems of proactively reacting to a futuresituation that is predicted by an analysis of a stream of events of anevent processing component, such as an EPN. The proactive reaction, alsoreferred to as a proactive action, is optionally selected from a set ofoptional proactive reactions and/or using rules defined according tooptional proactive reactions. The selection is optionally made and/orthe rule is optionally set according to the effect of each proactiveaction on the probability of occurrence and/or cost of occurrence of thefuture situation and/or cost of the action execution. Optionally, thetime for performing the proactive action is also calculated in a mannerthat improves, for example optimizes, a utility function that refers tothe probability of occurrence of the future situation and/or the cost ofoccurrence of the future situation and/or the cost of executing theaction. For example, a utility function may be, but is not limited tothe expected costs of the event occurrence and the action execution,conditioned on the fact that a particular action is selected for aparticular time.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Reference is now made to FIG. 1, which is a flowchart of a method 100 ofproactively reacting to a future situation, such as desired and/orundesired events in an event processing component, according to someembodiments of the present invention. The future situation is detectedby analyzing a plurality of events carried out by the event processingcomponent and detecting of a current event pattern, also referred to asa predictive pattern accordingly.

The event processing component is intended to refer to acomputer-related entity, a hardware or hardware simulation, acombination of hardware simulation and software, software, or softwarein execution, wherein a plurality of events are processed by one or moreEPAs, optionally in an EPN architecture. For example, an eventprocessing component may be, but is not limited to, a process intendedto run on a processor, an executable, a thread of execution, and/or aprogram. By way of illustration, both an application running on a serverand the server can be an event processing component. One or more eventprocessing components may reside within a process and/or a thread ofexecution and an event processing component may be localized on onecomputer and/or distributed between two or more computers.

The method 100 is carried out, for example executed, by a computing unitor system, such as a local computing unit or system, for example adesktop, a laptop and/or a remote computing unit, for instance in acloud computing architecture, or a network environment using aclient-server configuration.

Reference is also made to FIG. 2A which illustrates a relational view ofsoftware components of a system 60 for proactively reacting to a futuresituation that is detected according to a predictive pattern appearingin a stream of events carried out by an event processing component,according to one embodiment of the invention. As shown, softwarecomponents include an input interface 61, a monitoring module 62, adetection module 64, and a predicting module 65. The lines in FIG. 2Adepict optional and non limiting data flow between the modules.

The system 60 may be used as a proactive event-driven system thatexploits existing event-based technology to monitor current events andinfer future situations based on that information. An exemplary model isdescribed in FIG. 2B, which is a schematic illustration of a model thatis operated according to the predictive pattern“detect-forecast-decide-act” to proactively react to future situation ofa physical system 77 based on analysis of its events in real time,according to some embodiments of the present invention. First, as shownat 71, predictive patterns are detected, for example by processingcurrent event streams, which may indicate a situation in the future.Optionally, an event-processing engine 72 that is modified and enhancedto handle uncertain and future events is used. Now, as shown at 73, oneor more future situations, such as events, that need to be eliminated,mitigated, or intensified are predicted. This may be done based onhistorical data or any other predictive calculation. This ability isoptionally implemented using suitable syntax and semantics of eventderivation as supported in most of the common event processing systems.Forecasted events may be further used as input for predictive patterndetection. Now, as shown at 74, suitable proactive action(s) that may betaken ahead of the time of the forecasted event in order to eliminate,mitigate, or intensify the future situation are selected. This isoptionally done using real-time decision making or policy adjustmentcapabilities, for example as described below, with or without humanintervention. For example, the decision may be done automatically and/orsemi automatically, for instance based on recommendation(s), and/ormanually, for instance by an operator 75. Now, as shown at 76, theselected proactive action(s) are taken, optionally by an event-drivenplatform that makes explicit distinction between consumers andactuators. When the future situation is one or more positive events oropportunities, the proactive system makes sure that the chances thefuture situation occur increase (for example, a gain opportunity inalgorithmic trading).

According to some embodiments of the present invention, an eventprocessing component, such as an EPN component is adjusted to reactproactively to future situations which are dedicated in advance. Inorder to facilitate such proactive decision making, several extensionsare made to the EPN architecture. In standard event-processing, events(raw and derived) are transmitted between nodes in the network. Typicalderived events are not directly observable, but are inferred by theevent processing system. Forecasted events, in contrast, may be derivedand observable. Optionally, in these embodiments at least some of theevents are forecasted events wherein each has an occurrence time thatmay be derived and set to a future time, meaning the occurrence time ofthe derived event is later than its detection time. Both the ability toderive occurrence time and set it to future time is an extension for thenotion of derived event. Both the occurrence of the forecasted event,and the time of occurrence, may be uncertain and therefore characterizedprobabilistically.

Optionally, a proactive agent (PRA) is added to the event processingmodel of the EPN. The PRA is optionally an EPA in that it receivesevents as input and its activation is triggered by the arrival of suchan event. The PRA optionally encapsulates a decision logic that mayrange from using predetermined decision trees to solving amulti-objective optimization in highly dynamic environments, for exampleas described below.

Optionally, an additional type of a player is added to the eventprocessing primitives: actuator. Unlike consumer which receives events,actuator receives directives to perform actions, where theresponsibility on the selection of the action and its activation timingis part of the proactive model. Thus, the proactive actions are anintegral part of the model along with their relevant characteristics.Note that actuators may be either autonomous actuators, or softactuators that require human in the loop.

First, as shown at 101, the system 60, for example the input interface61, captures a plurality of events, for example an event stream, carriedout by an event processing component to be inspected by the monitoringmodule 62. The monitoring is performed in real time, during theoperation of the event processing component.

Then, as shown at 102, a current event pattern predictive of a futuresituation is detected by analyzing the events, for example by thedetection module 64. The predictive pattern may be indicative of afuture situation, optionally inevitable, optionally problematic ordesired. Future situations may be negative, for example systemmalfunctions, or positive events, for example opportunities. In such anembodiment, the goal may be to select a proactive action that increasesthe chances that the opportunity is captured (for example, a gainopportunity in algorithmic trading). The future situation may be theoccurrence of a single forecasted event, referred to herein as a targetevent, or a set of events, for example a sequence. It should be notedthat most examples herein related to avoiding and/or reducing the effectof negative events; however, increasing the probability of occurrence ofpositive events may be similarly encouraged.

As used herein, a future situation is defined as one or more futureevents which the occurrence thereof is forecasted by the detection ofone or more predictive patterns (with probabilistic characterization ina form of distribution over a time range), there is a finite (relativelysmall) set of feasible proactive actions, optionally known at designtime, for potentially reducing and/or increasing the probabilitythereof, and there one or more cost functions associated both with thefuture situation and with each feasible proactive action.

As demonstrated by Y. Engel and O. Etzion. Towards proactiveevent-driven computing. In Proceedings of the 5th ACM internationalconference on Distributed event-based system, pages 125-136, 2011,proactive event-driven computing may be applied in many differentdomains for inherently different applications. For clarity, a proactiveaction is an action that:

-   -   1. aims to eliminate a forecasted event or to mitigate or        increase the effect of the forecasted event;    -   2. there are one or more predictive patterns, whose occurrence        forecasts that the forecasted event will occur (with        probabilistic characterization in a form of distribution over a        time range); and    -   3. there are one or more cost functions associated with the        forecasted event and/or the action.

The proactive action is optionally part of a finite set of feasibleactions, known at design time, each potentially reducing the probabilityof the forecasted event.

As shown at 103, proactive actions which may be performed to decrease orto increase a probability of occurrence of the future situation areidentified. For example, the system 60 includes a database 63 with a setof records, each associate between a future situation and one or moreoptional proactive actions. In use, a record associated with theidentified future situation may be retrieved from the database and theone or more possible proactive actions may be extracted.

As shown at 104, for each proactive action, a probability of occurrenceof the future situation after the proactive action is taken is provided,for example calculated or extracted from a database. For example, thisprobability of occurrence is a probability distribution over theoccurrence time of the future situation conditioned on the detection ofthe predictive pattern. This distribution predicts the probability thatthe future situation, denoted herein as ε, occurs within a specific timeframe of the detection of predictive pattern. To describe thisprobability we employ terminology from survival analysis: an eventdensity function (EDF) of future situation, denoted here by g^(ε)(t),indicates the probability that ε occurs at time t, which is heremeasured relative to the detection of ε. The cumulative distributionfunction (CDF) of g is denoted by G^(ε)(t), and is called the lifetimedistribution function (LDF) of ε. G^(ε)(t) indicates the probabilitythat ε occurs between a time zero and t. By using the terms EDF and LDF,we assume that ε is bound to occur between now and eternity. This istrue for most cases (events such as machine failure, resource depletion,or even traffic jam); when this is not the case g^(ε)(t) may be replacedwith two parameters: p^(ε) denotes a probability of ε ever occurring,and f^(ε)(t) denotes EDF: a distribution over the time ε occurs, giventhat ε occurs until eternity. In that case the following is setg^(ε)(t)=f^(ε)(t)p^(ε) and proceed as before.

Obtaining the EDF allows association with survival analysis and providesgeneral guidelines on how this could be done. Assuming the availabilityof past data, in which both ε and the predictive pattern, denoted hereinas π, may be identified and timestamped, an EDF relative to theoccurrence time of π may be obtained using a standard regression model;however, practical models ultimately differ significantly betweendomains.

A possible proactive action α is applied to reduce or increase theprobability of an undesired event. If the application of α before ε doesnot prevent ε from happening with certainty, α is associated with a newevent density function g_(α) ^(ε)(t). Now, g^(ε)(t) is the probabilitythat ε occurs at t, assuming no action is taken until time t, whereasg_(α) ^(ε)(t) is the probability that ε occurs at t, assuming α is takenuntil t. FIG. 3 is an exemplary probabilistic structure wherein ε ismeasured according to the distribution g^(ε)(t). At time t_(α)α isapplied and afterwards the probability is measured according to g_(α)^(ε)(t). As indicated in FIG. 3, the probability that ε occursthroughout the time window is measured under the combination of the two,depicted by the broken line.

This model assumes that α reduces the probability that ε occurs and thisis independent of the processes causing ε. For example, an action thatadds computing resources to the server is independent of the steadyincrease of incoming requests during a DoS attack; the latter has lowerprobability to cause a failure if resources are added, as long as thisaction is taken prior to the failure. An alternative model, in whichthere is dependency between the two, is also possible. Under thealternative model, the distribution g_(α) ^(ε)(t) is taken with respectto the time α is taken. The language and the algorithms presented hereinmay be adapted to the second model.

As shown at 105, for each proactive action, a cost of the occurrence ofthe future solution with, after, and/or before the respective proactiveaction is provided, for example calculated or extracted from a database.This allows taking informed decisions hinged on the ability to measurethe cost. Optionally, the cost takes into account a combination betweenthe cost of the event in case it occurs and the cost of performing therespective proactive action.

For brevity, an either positive or negative reward is assumed to beassociated with ε and a cost function associated with each action.Optionally, a cost of an action is calculated as a function of time asactions often effect operation until some specific future time (forexample, shutting down the server until the danger is over, or takingmachinery down to maintenance and losing the rest of the working week).In that case, the cost is a decreasing function in the activation time.If the cost of a proactive action is constant, than the choice isbetween not activating it and activating it immediately when the rule istriggered; there is no point in waiting. If the cost of a proactiveaction is zero, the proactive action is applied automatically when arelevant rule (according to which the proactive action reduces theprobability of an undesirable event, or increases the probability of adesired event) is triggered.

For particular applications, the cost function may express slightlydifferent meanings. For example, if α cannot prevent ε, but it reducesthe cost of its occurrence, optionally with certainty, then the cost ofthe proactive action may include the cost of the occurrence of the eventafter the application of α. Similarly, the cost of ε may include thecost of applying α after the event ε occurred (in case we did not manageto time α as a preventive measure before ε, but the proactive action isstill required as a remedy), or any difference between the benefit ofapplying α before vs. after ε occurs.

For example, a certain stock is set to be sold at a certain price ifsome negative event happens in the market; selling the stock before theevent occurs is much more beneficial than after it happens. In such casea cost, denoted as C, represents an estimation of the amount the stock'svalue loses when the event occurs.

The notion of proactive situation contains the information needed forthe proactive action to receive its decisions. Formally:

A proactive situation where a prediction of future events based oncurrent events, may be denoted by a tuple σ=(ε; C_(ε), T; T; C), where εis an event, C_(ε) denotes the cost incurred by the occurrence of ε, Tdenotes an expiration time, and τ is a set of responses.

A response

∈

is a tuple (α; g^(ε)α; δ_(α)) where α denotes an action identifier,denotes g^(ε)α a density function of ε given that α is in effect, andδ_(α) denotes a delay namely the time it takes to α as a function oftime it is applied. τ always includes one or more responses (i.e.default responses) in which the proactive action α is no action. Such aresponse may be denoted as (∅,g^(ε)(t),0,p^(ε)). Finally, the costfunction C_(α):[0,T]

indicates the cost of performing α as a function of the time.

Optionally, the detection of a certain predictive pattern indicates ahigher risk for the occurrence of ε in a future period where T denotes aparameter that defines the future period in the context of thepredictive pattern. After T, the effect of detecting the predictivepattern fades, and the probability of occurrence of ε returns to normal.Beyond that, T may be used to indicate a waiting period before anapplication of an action is costless (for example, the end of a minute,an hour, working day, a week, a month or any intermediate or longerperiod). Optionally, T bounds the horizon of the decision problem. T maybe specified relative to the detection of the predictive pattern, or asan absolute time. In the latter case the relative T is calculated eachtime the rule is triggered.

The predictive pattern is optionally not a precondition to ε. Mostsystems support a default policy that works well as long as no rarenegative event occurs. Under those normal conditions, the rare event ofinterest (ε), may have some non zero probability to occur. The proactivemodel allows such a system to adapt its policy automatically when theprobability of a future event changes, optionally significantly (i.e.above a predefined threshold), see setting in R. Brafman, C. Domshlak,Y. Engel, and Z. Feldman, Planning for operational control systems withpredictable exogenous events, in AAAI, 2011. Optionally, a proactiveaction includes a combination of a plurality of proactive actions.

Now, as shown at 106, one or more of the plurality of proactive actionsare selected according to the probability of occurrence and optionallythe cost, which are calculated per proactive action, optionallycontinuously during a period lasting until the future situation occurs.For brevity, reference is made to a selected proactive action; however,a selected proactive action may be a combination of different singleproactive actions.

Optionally, 104-106 are implemented as a set of rules that defines whichproactive action should be taken in response to a certain predictivepattern. In such an embodiment, the rules may be generated in advance,as described below based on the above. The rules may be documented in aPRA, for example as described above.

For example, the selection is made according to a rule that indicatesthat if a predictive pattern is detected, there is a specificprobability that it is followed by some event ε with cost C_(ε). A rulealso specifies actions that may be taken to change that probability, andalso the distribution of the occurrence time. The question is “what isthe optimal decision given this new information?”. Optionally, adecision includes not only which action to take, but also when to takeit. The assumption is that the cost of taking the proactive actionchanges over time. In many cases, the proactive actions involve someform of shutdown of a machine or a service, until some fixed time (withrespect to the detection of the predictive pattern) for example the endof a working day or the time in which the danger of attack is back tonormal. When this is the case, the optimal timing to perform a proactiveaction may be later than the time predictive pattern is detected.Optionally, this problem of taking action ahead of time, underuncertainty, is viewed from a decision theoretic perspective so that adecision is taken by optimizing a utility in expectation, weighing therisk of doing nothing when the negative event eventually occurs (falsenegative), against the cost of taking action when the event does noteventually occur (false positive).

According to some embodiments of the present invention, the time ofapplying the proactive action is taken as part of the proactive actiondefinition, hence making the proactive action space continuous insteadof a state space. This solves the problem of continuity. Optionally, adecision is taken in only one state, and thus the continuity of theproactive action space allows obtain an optimal policy using continuousoptimization methods. For example, the decision is made according to aMarkov decision process (MDP). For brevity, MDP is denoted herein asM=tuple (S_(M);A_(M); T_(M);R_(M)) where S_(M) denotes a set of states,A_(M) denotes a set of optional proactive actions, for example theoptional proactive actions identified for the future situation,R_(M):S→R denotes a reward function, and TM:S_(M)×A_(M)×S_(M)→[0;1]denotes a transition function with T_(M)(s; α; s′) capturing aprobability of reaching s′ by applying action α∈A_(M) in state s; foreach (s;α)∈S×A_(M),

$\mspace{79mu}{{\sum\limits_{s^{\prime}\epsilon\; S_{\mathcal{M}}}{T_{\mathcal{M}}\left( {s,\alpha,s^{\prime}} \right)}} = 1}$

It should be noted that a solution to MDP is a policy a mapping ψ:

from states to actions; with a policy, an EPA of the event processingcomponent knows which optimal action to perform in any state it reaches.Because transitions are uncertain, the actual sum of rewards a policyobtains is not known in advance; a potentially different reward may beobtained in each execution. A policy is therefore evaluated according toits expected utility: the expectation on the value of the randomvariable defined as the sum of rewards obtained by using the policy.This expectation can be defined recursively using the following Bellmanequation. U^(ψ)(s) denotes an expected utility of a policy, startingfrom state s; then:

$\begin{matrix}{{U^{\psi}(s)} = {{R_{\mathcal{M}}(s)} + {\sum\limits_{s^{\prime}}{{U^{\psi}\left( s^{\prime} \right)}{\mathcal{T}_{\mathcal{M}}\left( {s,{\psi(s)},s^{\prime}} \right)}}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

where a reward is received in a current state.

Reference is now made to an exemplary MDP solution for calculating adecision. For clarity, a single possible action α where δα=0 is assumed.The space of solutions in such a setting may consist of four states, forexample as depicted in FIG. 4A. A normal state (S_(n)) is in place aslong as no rule is triggered. When π is detected, and hence the rule δ→σis triggered, the space moves to a alert state (S_(d)), a state whereinan occurring event denoted as S_(ε), and the state we reach afterapplying action α in state S_(d) is denoted as S_(α), to return to S_(n)at time T state. The policy maps each state to an action to be taken incase that state is reached. In this setting, and optionally only in thissetting, in S_(d) a decision among several courses of proactive actionsin S_(d) is taken.

Optionally, the time of the proactive action is set by the policy sothat the policy determines an action-time pair: which action to take, atwhat time. Given a single α, the policy determines whether to apply α,and if so when to apply it (t_(α)). As depicted in FIG. 4A, the arrowmarked with α and exiting state S_(d), represents not a single possibleaction, but a space of possible actions (α;t_(α)). The transition modelof taking action (α;t_(α)) in state S_(d) is as follows: with someprobability the event occurs before α is taken and the state changes toS_(α) and with a complementary probability the future situation does notoccur before the proactive action is taken and hence the S_(α).Optionally, in state ε (S_(ε)) penalty of C_(ε) is incurred. In S_(α),the only possible action is no action. From there the event may occurwith some lower probability (results in S_(ε)) or not occur with thecomplementary probability (results in moving to state S_(n) at time T).It is also possible to take no action in S_(d) and to transition toS_(ε) with some probability and to S_(n) with a complementaryprobability.

Optionally, in order to optimize the MDP policy, an expression thatevaluates any given policy is built and used to determine the value ofstate S_(d) under any policy; the policy that maximizes this value as anoptimal policy. In such a manner, the policy evaluation problem issimplified. As by time T the state is Sn, each round in which we reachS_(d) may be considered as an independent epoch. A policy that maximizesthe utility for a single epoch also achieves an optimal average utilityfor infinite horizon.

With the finite horizon, the MDP is solved using a backwards induction:where the utility of the states is calculated at the end of the horizon(only S_(n)); next, the utility of states is calculated one step back,using equation 1, and continue back in time until the utility of thecurrent state at the current time is obtained. This allows building anexpression that evaluates a policy ψ under a specific model. Initially,we consider any ψ under which α is applied (rather than taking “noaction”).

As there is no cost (or benefit) at S_(n), U^(ψ) (S_(n))=0. In S_(α) apenalty of C_(α) is incurred so that U^(ψ)(S_(n))=−C_(ε). In S_(α) apenalty of C_(α) (t_(α)) is incurred, and given the probability to moveto S_(ε) the policy evaluation of equation 1 yields:U ^(ψ)(S _(α))=−C _(α)(t _(α))+

(S _(α) ,S _(ε))(−C _(ε))  Equation 2

Finally, in state S_(d) there is no penalty in the state itself, and theutility is computed Equation 1 as follows:U ^(ψ)(S _(d))=

(S _(d) ,S _(α))U ^(ψ)(S _(α))+

(S _(d) ,S _(ε))U ^(ψ)(S _(ε))=−

(S _(d) ,S _(α))(C _(α)(t _(α))+

(S _(α) ,S _(ε))(C _(ε)))−

(S _(d) ,S _(ε))C _(ε)  Equation 3

FIG. 4B depicts how this model is extended to multiple actions, and tonon-zero delays. A state S_(αi) is designated for each action αi andadds a state S_(δi) associated with the delay of each action. As in FIG.4A, an arrow marked αi (e.g. α₁ and α₂) represents a space of possibleactions (αi; t_(αi)). When extending the backward induction to includethe extensions the detailed process may be omitted, and the result whichis analogous to the following:Equation 4:U ^(ψ) ^(i) (S _(d))=−

(S _(d) ,S _(δ) _(i) )(C _(α) _(i) (t _(α) _(i) )+C _(ε)(

(S _(δ) _(i) ,S _(ε))+

(S _(δ) _(i) ,S _(α) _(i) )

(S _(α) _(i) ,S _(ε))))−

(S _(d) ,S _(ε))C _(ε)  (4)

where ψ⁰ denotes a policy of taking no action. Simple backward inductionfor this policy yields:Equation 5:U ^(ψ) ⁰ (S _(d))=

(S _(d) ,S _(n))U ^(ψ) ⁰ (S _(n))+

(S _(d) ,S _(ε))U ^(ψ) ⁰ (S _(ε))=−

(S _(d) ,S _(ε))C _(ε)  (5)

The optimal policy ψ* may be obtained by maximizing U^(ψi)(S_(d)) overall pairs (α_(i); t_(αi)) and comparing with U^(ψ0)(S_(d)), for exampleas follows:

$\begin{matrix}{\psi^{*} = {\arg\;{\max\limits_{{\psi \in \psi_{i}},\psi^{0}}{U^{\psi}\left( S_{d} \right)}}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

Briefly stated, the results of backward induction are the formulas forexpected utility functions (see equations 4 and 5). Therefore, optimallytaking the decision may be done by finding a maximum of these functionsfor each action, and choosing the best between them, for example asdescribed in Equation 6.

Optionally, expressions are obtained for transition probabilities thatallow expressing equations 4 and 5 in terms of the components of aproactive situation to enable solving equation 6. Optionally, thetransition probabilities are all derived from the probability of theevent occurrence. As set above, ψ is the policy to apply action α attime t_(α) conditioned of course on the event ε not occurringbeforehand. P^(ε)(t₁; t₂) denotes a probability that ε occurs within thetime interval (t₁; t₂), conditioned on not occurring until time t₁. Theconditioning allows defining being in a certain state, allowingmeasuring transition probabilities from a state s when in state s.Furthermore, P^(ε) _(α)(t₁; t₂) denotes the same probability asP^(ε)(t₁; t₂), assuming the proactive action α has taken effect exactlyat time t₁. P^(ε)(t₁; t₂) may be calculated in terms of the parameterswe model in the language.

Based on the above definitions, the following take place:

${P^{\epsilon}\left( {t_{1},t_{2}} \right)} = \frac{{G^{\epsilon}\left( t_{2} \right)} - {G^{\epsilon}\left( t_{1} \right)}}{1 - {G^{\epsilon}\left( t_{1} \right)}}$

where for P^(ε) _(α)(t₁; t₂) the conditioning (denominator) takes intoaccount the fact that until the proactive action occurrence at t₁, thedistribution in place was G^(ε):

${P_{\alpha}^{\epsilon}\left( {t_{1},t_{2}} \right)} = \frac{{G_{\alpha}^{\epsilon}\left( t_{2} \right)} - {G_{\alpha}^{\epsilon}\left( t_{1} \right)}}{1 - {G^{\epsilon}\left( t_{1} \right)}}$

where t₀ denotes a time of detection of π (or the time of transitioningfrom S_(n) to S_(d)). The transition probabilities from S_(d) to S_(ε)or S_(δ) or S_(δi) are:

(S _(d) ,S _(ε))=P ^(ε)(t ₀ ,t _(α) _(i) )

(S _(d) ,S _(δ) _(i) )=1−P ^(ε)(t ₀ ,t _(α) _(i) ).

To proceed from δi to a probabilities are as follows:

(S _(δ) _(i) ,s _(α) _(i) )=P ^(ε)(t _(α) _(i) ,t′ _(α) _(i) ),

where t′_(αi)=t_(αi)+δ_(αi). The state changes S_(αi) if ε does notoccur between the time the proactive action is applied and the time ittakes effect.

The transition from S_(δi) to S_(ε) occurs with the complementaryprobability. Finally, for ψ_(i), the distribution over the eventoccurrence in S_(αi) is denoted by:

(S _(α) _(i) ,S _(ε))=P _(α) _(i) ^(ε)(t _(α) _(i) ,T)

where P^(ψ0)(S_(d), S_(n)) is a complementary probability. Whensubstituted in equation 4, an expression for the utility of any policy ψand the policy ψ⁰ is obtained as follows:Equation 7:U ^(ψ) ^(i) (S _(d))=(1−P ^(ε)(t ₀ ,t _(α) _(i) ))(−C _(α) _(i) (t _(α)_(i) )−C _(ε)(P ^(ε)(t _(α) _(i) ,t′ _(α) _(i) )+(1−P ^(ε)(t _(α) _(i),t′ _(α) _(i) ))P _(α) _(i) ^(ε)(t′ _(α) _(i) ,T)))−P ^(ε)(t ₀ ,t _(α)_(i) )C _(ε)  (7)

According to some embodiments of the present invention, the monitoredevents are uncertain events. For instance, the events are based on noisysensor readings. This means that input events based on which apredictive pattern detection is issued are uncertain; this can berepresented using a measure of probability of occurrence on the eventitself. The uncertainty affects the probability the events can predictof future situation. As explained above, predictive patterns are complexcombinations of input events. Optionally, the predictive patternlanguage is indicative of the probability that the predictive pattern isdetected as a function of the probability of the input events, forexample by attaching a probability measure to each event. For example,consider a predictive pattern π with two event types, E₁ and E₂, fromtwo independent sources. The predictive pattern is the appearance ofboth E₁ and E₂ are encountered during a given time window. Assume twouncertain instances are detected, one of each event, denoted by e₁ ande₂ and carry probability measures P(e₁) and P(e₂). Given independence ofrandom variables e₁ and e₂, the probability to encounter the sequence isP_(π)=P(e₁)P(e₂). This calculation becomes more complicated whenmultiple observations e^(j)i and e^(k)i regarding the same event typee_(i) occur. When e^(k)i overrides e^(j)i namely updates the probabilitythat e_(i) occurred, the latest observation also determines theoccurrence time of the prospect event. With this semantics a singleinstance calculation may be resorted back as above. In another case, fora specific event type e_(i), independent observations to two different(possible) instances of e_(i) are made: (e^(j)i; P(e^(j)i)) at timet_(j), and (e^(k)i; P(e^(k)i) at time t_(k). The observations may bedistinguished using instance identifiers (IDs).

For the case of independent observations the probability and occurrencetime of an instance has to be determined. For simple predictive patternsthis probability may be computed analytically. For example, whendetecting the sequence (e¹ ₁; e² ₁; e₂), thenP _(π)=(P(e ₁ ¹)+P(e ₁ ²)−P(e ₁ ¹)P(e ₁ ²))P(e ₂)

The probability of complex predictive patterns may be computed bysampling. N series of instances is generated by sampling from theprobability of the events in the sequence. The number of samples iscounted, out of the N series, in which π occurred.

This ratio is an estimate of P_(π).

Continuing the example above, consider the input stream (e¹ ₁; e² ₁; e¹₂; e² ₂) the detection of π occurs twice: once after receiving e¹ ₂ andagain after receiving e² ₂. The probability P_(π) now combines theprobability of the two observations, e¹ ₂, e² ₂ but it is not obvious todetermine the occurrence time t₀ that should be associated with π. Thisoccurrence time is of course instrumental in determining the timing of apossible action. A reasonable solution is to take expectation over thetime (an average over the time stamps from the candidate observations,each weighted by the probability associated with that observation).

Finally, the semantic of g^(ε)(t) is the probability of ε given that thepredictive pattern π occurred. g′_(ε)(t)=g^(ε)(t)P_(π) may now bedefined to get the probability of ε given the probabilistic observationof π. And g(t) may be replaced with g′_(ε)(t) in any reasoning task.

According to some embodiments of the present invention, the probabilityof the future situation ε is continuously calculated as a function ofone or more dynamic characteristics. An exemplary characteristic istemperature where a higher temperature is identified with a highermalfunction rate, the probability of occurrence is updated continuouslyand respectively according to the measured temperature.

For example, π_(x) denotes a parameterized predictive pattern where πdenotes a predictive pattern and x denotes a number of rejections thatwere actually encountered within a certain context. As an example, LDFis assumed as exponential and parameter x may affect the steepness ofthe distribution as follows:G ^(ε,x)(t)=1−e ^(−tx)

where parameters are specified within a predictive pattern definition(possibly with lower or upper bounds) and used in the functionalexpression of G^(ε) and G^(ε)α.

It should be noted that the actual probability of ε is associated with aparticular rule and therefore G^(ε) and G^(ε)α are calculated in realtime.

In use, when a nine is triggered by identification of a predictivepattern, actual probability distributions arc calculated. Optionally, aproactive agent encapsulates this logic and executes a reasoningalgorithm any time it receives a new forecasted event whose probabilityis different than before. The algorithm is executed each time either anobservation is received with new probability, or an event changes aparameter in a previously detected predictive pattern. Each time thealgorithm is executed, the action-time pair (α; t_(α)) it prescribes mayof course be updated. In particular, it is possible that we decide toperform α at time t_(α), and before time t_(α), the action time ispostponed. An action is therefore performed in the earliest time t inwhich t=t_(α); that is, when the time to perform an action is reachedwithout an event occurring in between causing this time to be postponed.The methods as described above are used in the fabrication of integratedcircuit chips.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

It is expected that during the life of a patent maturing from thisapplication many relevant methods and systems will be developed and thescope of the term a processor, a database, and an interface is intendedto include all such new technologies a priori.

As used herein the term “about” refers to ±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”. This termencompasses the terms “consisting of” and “consisting essentially of”.

The phrase “consisting essentially of” means that the composition ormethod may include additional ingredients and/or steps, but only if theadditional ingredients and/or steps do not materially alter the basicand novel characteristics of the claimed composition or method.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

The word “exemplary” is used herein to mean “serving as an example,instance or illustration”. Any embodiment described as “exemplary” isnot necessarily to be construed as preferred or advantageous over otherembodiments and/or to exclude the incorporation of features from otherembodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

What is claimed is:
 1. A method for predicting a future situation basedon an analysis of at least one predictive pattern, comprising:monitoring a plurality of events carried out by an event processingcomponent; detecting a predictive pattern predictive of a futuresituation; selecting one of a plurality of proactive actions and anexecution time for said selected proactive action according to itseffect, when executed in said execution time, on at least one of aprobability of occurrence and a cost of occurrence of said futuresituation; and outputting said selected proactive action; wherein saidplurality of proactive actions are documented in a database anddesignated for potentially reducing or increasing a probability ofoccurrence of said future situation.
 2. The method of claim 1, whereinsaid selecting is performed according to a rule defining said selectedproactive action as a response to said predictive pattern.
 3. The methodof claim 1, wherein said selecting comprises calculating for each saidproactive action in at least one execution time a combination betweensaid probability of occurrence of said future situation and said cost ofoccurrence of said future situation and performing said selectingaccording to said calculating.
 4. The method of claim 1, wherein saidcost of occurrence changes as a function of time during a period lastinguntil said future situation is estimated to occur, said selectingcomprises calculating said effect in a plurality of instances duringsaid period.
 5. The method of claim 1, wherein said probability ofoccurrence changes as a function of time during a period lasting untilsaid future situation is estimated to occur, said selecting comprisescalculating said effect in a plurality of instances during said period.6. The method of claim 1, wherein said cost changes as a function oftime during a decision epoch.
 7. The method of claim 1, wherein saidprobability of occurrence dynamically changes during said monitoringwhile said future situation is continuously calculated as a function ofat least one dynamic characteristic of said event processing componentand dynamic parameter that effects said event processing component. 8.The method of claim 1, wherein said predictive pattern defines at leastone event marked with a measure of probability of event occurrence;wherein said probability of occurrence of said future situation iscalculated according to said measure of said at least one event.
 9. Themethod of claim 1, wherein said selecting comprises calculating saideffect for each said proactive action in real time, in response to saiddetecting.
 10. The method of claim 1, wherein said selecting comprisesestimating said effect for each said proactive action in a plurality offuture instances; wherein said selecting comprises selecting in which ofsaid plurality of future instances to perform said selected proactiveaction.
 11. The method of claim 10, wherein said cost of occurrencecalculated by taking into account a cost of performing said selectedproactive action at a respective said future instance.
 12. The method ofclaim 1, wherein said cost of occurrence is a combination of a firstcost of performing said selected proactive action and a second cost ofthe effect of said future situation on the activity of said eventprocessing component after said selected proactive action is performed.13. The method of claim 1, wherein said probability of occurrence is aprobability distribution over an occurrence time of said futuresituation conditioned on the detection of said predictive pattern. 14.The method of claim 1, wherein said selecting comprises applying aMarkov decision process (MDP) on said plurality of proactive actions.15. The method of claim 14, wherein said MDP is solved using a backwardsinduction.
 16. The method of claim 1, wherein said selected proactiveaction is a combination of a plurality of proactive actions.
 17. Asystem of predicting a future situation based on an analysis of at leastone predictive pattern, comprising: a processor; a monitoring whichmonitors an event processing component carrying out a plurality ofevents; a detection module which detects a predictive pattern predictiveof a future situation; a database which stores a plurality of recordseach of one of a plurality of proactive actions designated forpotentially reducing or increasing a probability of occurrence of saidfuture situation; and prediction module which identifies a plurality ofproactive actions each having an effect on at least one of a probabilityof occurrence and a cost of occurrence of said future situation andselects, using said processor, one of said plurality of proactiveactions according to said effect.
 18. A computer program product forpredicting a future situation based on an analysis of at least onepredictive pattern, comprising: a non transitory computer readablestorage medium; first program instructions to monitor a plurality ofevents carried out by an event processing component; second programinstructions to detect a predictive pattern predictive of a futuresituation; and third program instructions to select one of a pluralityof proactive actions and an execution time according to its effect on atleast one of a probability of occurrence and a cost of occurrence ofsaid future situation; wherein said first, second, and third programinstructions are stored on said computer readable storage medium;wherein said plurality of proactive actions are documented in a databaseand designated for potentially reducing or increasing a probability ofoccurrence of said future situation.
 19. A method for predicting afuture situation based on an analysis of at least one predictivepattern, comprising: monitoring a plurality of events carried out by anevent processing component; detecting a predictive pattern predictive ofa future situation; identifying a predefined rule that defines which ofa plurality of proactive actions to take in response to said detectionfrom a plurality of predefined rules; and performing said proactiveaction in response to said detection; wherein said predefined rule isautomatically calculated according to an effect of said proactive actionon at least one of a probability of occurrence and a cost of occurrenceof said future situation in relation to the effect of other saidplurality of proactive actions; wherein said plurality of proactiveactions are documented in a database and designated for potentiallyreducing or increasing a probability of occurrence of said futuresituation.